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Evaluation of phantom finger sensation evoked by electrical stimulation for transradial amputee: A case study

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Evaluation of phantom finger sensation evoked by electrical ...

2021 IEEE International Conference on Real-Time computing and Robotics, RCAR 2021

作者： Wang, Yingying Tang, Xi Jiang, Naifu Tian, Lan Zheng, Yue Li, Xiangxin Xie, Jun Li, Guanglin Fang, Peng Shenzhen Institutes of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China School of Mechanical Engineering Xi'An Jiaotong University Xi'an710049 China

ISBN: (纸本)9781665436786

Prostheses are very necessary for limb amputees to restore their lost motor functions. However, most of the commercially available prostheses are lack of intuitive sensory feedback, which would limit the prosthetic control performance. Electrical stimulation is supposed to induce sensory feedback for limb amputees, and some invasive methods have been reported but limited due to the second surgery and possible biological incompatibility in long-term use. Transcutaneous Electrical Nerve Stimulation (TENS) is considered as a non-invasive method to reestablish neural feedback for limb amputees, which might be more practical in clinic application. In this paper, we proposed an approach based on electroencephalogram (EEG) to study the relationship between the TENS parameters and the evoked somatosensory information for limb amputees. The experimental results demonstrated that different somatosensory information evoked by TENS at different body positions on a transradial amputee could be distinguished by the Event-Related Potential (ERP) characteristics such as frequency range, brain electrical activity mapping, latency, and shape feature of ERP segments. In addition, the high correlation of ERP curves obtained by the repeated experiments could approve the effectiveness and stability of our proposed method. This work might support the future research on non-invasive sensory neural feedback for limb amputees. © 2021 IEEE.

关键词： Phantoms

Learning Compliant Assembly Strategy From Demonstration

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Learning Compliant Assembly Strategy From Demonstration

IEEE International Conference on Real-time computing and Robotics (RCAR)

作者： Sheng Liu Juyi Sheng Yongsheng Ou Southern University of Science and Technology (SUSTech) Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems (#2019B121205007)

Compared with robots, humans can complete the different assembly tasks of parts flexibly and quickly. By teaching robots with human experiences, not only the industrial assembling tasks can be resolved, but also many other robot applications can be realized. The pose adjustment stage is the most critical part of the peg-in-hole assembly process. This paper analyzes the contact force in the pose adjustment stage and calculates the control variables that affect the assembly motion. Based on the Gaussian mixture model (GMM), the nonlinear mapping relationship between the control variables and the state variables during the assembly is established using the human demonstration data, and the parameters of the model are solved by the expectation maximization (EM) algorithm, and thus the human-like compliant assembly is completed by a robot. In order to verify the effectiveness of the demonstration-learning algorithm, a peg-in-hole assembly experiment was carried out using KUKA manipulator. Finally, the experiment shows that the proposed learning-based method not only improves the efficiency of the robot peg-in-hole assembly but also makes the manipulator have a satisfied adaptive ability to the complex environments in the assembly process.

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Deterministic Algorithms for the Hidden Subgroup Problem

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arXiv 2021年

作者： Ye, Zekun Li, Lvzhou Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

We consider deterministic algorithms for the well-known hidden subgroup problem (HSP): for a finite group G and a finite set X, given a function f: G → X and the promise that for any g1, g2 ∈ G, f(g1) = f(g2) iff g1H = g2H for a subgroup H ≤ G, the goal of the decision version is to determine whether H is trivial or not, and the goal of the identification version is to identify H. An algorithm for the problem should query f(g) for g ∈ G at least as possible. Nayak [25] asked whether there exist deterministic algorithms with O(√|G|/|H|) query complexity for HSP. We answer this problem by proving the following results, which also extend the main results of Ref. [30], since here the algorithms do not rely on any prior knowledge of H. (i) When G is a general finite Abelian group, there exist an algorithm with O(√|G|/|H| |) queries to decide the triviality of H and an algorithm to identify H with O(√|G|/|H| log |H| + log |H|) queries. (ii) In general there is no deterministic algorithm for the identification version of HSP with query complexity of O(√|G|/|H|), since there exists an instance of HSP that needs ω(√|G|/|H|) queries to identify H.1 On the other hand, there exist instances of HSP with query complexity far smaller than O(√|G|/|H|), whose query complexity is O(log |G|/|H|) and even O(1). © 2021, CC BY.

关键词： Group theory

Confidence-guided adaptive gate and dual differential enhancement for video salient object detection

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arXiv 2021年

作者： Chen, Peijia Lai, Jianhuang Wang, Guangcong Zhou, Huajun School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Video salient object detection (VSOD) aims to locate and segment the most attractive object by exploiting both spatial cues and temporal cues hidden in video sequences. However, spatial and temporal cues are often unreliable in real-world scenarios, such as low-contrast foreground, fast motion, and multiple moving objects. To address these problems, we propose a new framework to adaptively capture available information from spatial and temporal cues, which contains Confidenceguided Adaptive Gate (CAG) modules and Dual Differential Enhancement (DDE) modules. For both RGB features and optical flow features, CAG estimates confidence scores supervised by the IoU between predictions and the ground truths to re-calibrate the information with a gate mechanism. DDE captures the differential feature representation to enrich the spatial and temporal information and generate the fused features. Experimental results on four widely used datasets demonstrate the effectiveness of the proposed method against thirteen state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection

Lightweight Multiple Perspective Fusion with Information Enriching for BERT-Based Answer Selection 1

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9th CCF International Conference on Natural Language Processing and Chinese computing, NLPCC 2020

作者： Gu, Yu Yang, Meng Lin, Peiqin School of Data and Computer Science Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University Guangzhou China

ISBN: (数字)9783030604509

ISBN: (纸本)9783030604493

Answer selection (AS), as one of the hottest topics in the field of natural language processing, has developed rapidly with outstanding performances reported, especially with the emergency of pretrained model (e.g., BERT). However, the current BERT based AS methods applied BERT only by fine-tuning or stacking other modules such as CNN and RNN, but ignored to exploit the discrimination embedded inside the BERT. In this paper, we proposed a novel method LMPF-IE, i.e., Lightweight Multiple Perspective Fusion with Information Enriching. The method can mine and fuse the multi-layer discrimination inside different layers of BERT and can use Question Category and Name Entity Recognition to enrich the information which can help BERT better understand the relationship between questions and answers. We test the proposed BERT layer-wised attention model in 5 benchmark datasets of answer selection task. The experimental results clearly verify better performances than the baseline models can be achieved by our method. © 2020, Springer Nature Switzerland AG.

关键词： Natural language processing systems

A method of Monocular Visual Odometry Combining Feature points and Pixel Gradient for Dynamic Scene 91

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A method of Monocular Visual Odometry Combining Feature poin...

91st IEEE Vehicular Technology Conference, VTC Spring 2020

作者： Li, Panwei Li, Huiyun Chen, Beizhang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems China

ISBN: (纸本)9781728152073

In the outdoor dynamic scene, the current Monocular Visual odometry methods of simultaneous localization and mapping system (SLAM) have Low utilization of image information, and they are not enough to meet the problem of stable image matching tracking. An information fusion method combining feature points and direct method gray matching method is proposed, which not only has no serious feature loss when demanding in fast motion, but also reduces the dependence on gray-scale invariant hypothesis. The proposed method also has better stability in the scenes with insufficient texture information, and the method reduces the dependence on gray-scale invariant hypothesis. Experimental results show that our novel front-end visual odometry simplifies the feature points, enriches the front-end utilization information, improves the pixel information of the image, and improves the feature points loss and excessive dependence on the intensity. The average real-time frame rate is around 32Hz, meeting the requirements of real-time performance. In the large-scale scene of the KITTI visual odometry datasets, the jitter effect of the dynamic scene is weakened, and the root mean square error of the positioning is 1. 79m, The average root mean square error of the method is reduced to 22.77% of ORB-SLAM, 26.48% of Semi-Direct Monocular Visual Odometry(SVO),and 37.68% of Large-scale direct SLAM(LSD). © 2020 IEEE.

关键词： Textures

Fast Coding Mode Prediction for Intra Prediction in VVC SCC

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Fast Coding Mode Prediction for Intra Prediction in VVC SCC

IEEE International Conference on Image Processing

作者： Dayong Wang Junyi Yu Xin Lu Frederic Dufaux Hongwei Guo Hui Guo Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des signaux et systèmes School of Engineering Honghe University Mengzi Yunnan China University of Electronic Science and Technology of China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by $34.95 \%$ on average while maintaining almost the same coding efficiency.

关键词： Video coding Deep learning Image coding Modulation Prediction algorithms Complexity theory Copper

High Efficiency Wiener Filter-based Point Cloud Quality Enhancement for MPEG G-PCC

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arXiv 2025年

作者： Wei, Yuxuan Wang, Zehan Guo, Tian Liu, Hao Shen, Liquan Yuan, Hui School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China School of Computer and Control Engineering Yantai University Yantai264005 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200072 China

Point clouds, which directly record the geometry and attributes of scenes or objects by a large number of points, are widely used in various applications such as virtual reality and immersive communication. However, due to the huge data volume and unstructured geometry, efficient compression of point clouds is very crucial. The Moving Picture Expert Group is establishing a geometry-based point cloud compression (G-PCC) standard for both static and dynamic point clouds in recent years. Although lossy compression of G-PCC can achieve a very high compression ratio, the reconstruction quality is relatively low, especially at low bitrates. To mitigate this problem, we propose a high efficiency Wiener filter that can be integrated into the encoder and decoder pipeline of G-PCC to improve the reconstruction quality as well as the rate-distortion performance for dynamic point clouds. Specifically, we first propose a basic Wiener filter, and then improve it by introducing coefficients inheritance and variance-based point classification for the Luma component. Besides, to reduce the complexity of the nearest neighbor search during the application of the Wiener filter, we also propose a Morton code-based fast nearest neighbor search algorithm for efficient calculation of filter coefficients. Experimental results demonstrate that the proposed method can achieve average Bjøntegaard delta rates of -6.1%, -7.3%, and -8.0% for Luma, Chroma Cb, and Chroma Cr components, respectively, under the condition of lossless-geometry-lossy-attributes configuration compared to the latest G-PCC encoding platform (i.e., geometry-based solid content test model version 7.0 release candidate 2) by consuming affordable computational complexity. © 2025, CC BY.

关键词： Wiener filtering

VCSE: Time-Domain Visual-Contextual Speaker Extraction Network

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arXiv 2022年

作者： Li, Junjie Ge, Meng Pan, Zexu Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan

Speaker extraction seeks to extract the target speech in a multi-talker scenario given an auxiliary reference. Such reference can be auditory, i.e., a pre-recorded speech, visual, i.e., lip movements, or contextual, i.e., phonetic sequence. References in different modalities provide distinct and complementary information that could be fused to form top-down attention on the target speaker. Previous studies have introduced visual and contextual modalities in a single model. In this paper, we propose a two-stage time-domain visual-contextual speaker extraction network named VCSE, which incorporates visual and self-enrolled contextual cues stage by stage to take full advantage of every modality. In the first stage, we pre-extract a target speech with visual cues and estimate the underlying phonetic sequence. In the second stage, we refine the pre-extracted target speech with the self-enrolled contextual cues. Experimental results on the real-world Lip Reading Sentences 3 (LRS3) database demonstrate that our proposed VCSE network consistently outperforms other state-of-the-art baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction